Coding, agents and Kimi tooling
Kimi's agentic coding strength, Agent Swarm, Kimi Code CLI, long autonomous runs, frontend/3D generation, and integrations like Hermes or Claude Code.
38%
Best tweets about Kimi
50 Best Tweets About Kimi AI (2026)
Read the best tweets about Kimi AI and Moonshot AI, including model releases, coding, agents, benchmarks, and practical experiments. Updated every two weeks.
Kimi and Moonshot AI product and model discussions, excluding unrelated people and products named Kimi.
- Creators
- 40
- Updated
Original Xholic analysis
What 50 top Kimi posts reveal
Discussion centers on Kimi K3, Moonshot's open-weight release of a 2.8T MoE with a 1M-token context. Posts highlight strong coding and frontend results versus closed models and a demand surge that paused new subscriptions. Counterpoints cover token inefficiency, slow serving, heavy self-hosting requirements, license terms, and distillation allegations posted by Director Michael Kratsios and relayed from OpenAI. Stance is mostly supportive (50%) or observational (38%), and 74% of posts carry media.
- Dominant tone
- Positive
- Median score
- 14.8
- Leading format
- Announcement
- Recent posts
- 82%
72% of posts
All-time engagement
38% of posts
Published in 90 days
Conversation map
The themes creators return to
Benchmarks vs closed frontier models
Benchmark results and head-to-head tests comparing Kimi models with Fable, Opus, GPT-5.6, plus skepticism about saturated benchmarks and harness differences.
30%
Open weights, licensing and self-hosting
K3 license terms, hardware needed to run it locally, creative CPU/DGX Spark deployments, and launch partners or cloud hosts like Modal and Ollama.
28%
Pricing, token efficiency and speed
API pricing, cost savings versus closed models, token inefficiency, slow serving latency, and expectations of inference cost drops.
24%
Kimi K3 launch and architecture
Release of Kimi K3 weights and tech report: 2.8T MoE, 896 experts with 16 active, KDA attention, 1M context, native vision, and efficiency claims.
16%
Demand surge and compute capacity
Subscription pause due to GPU limits, membership split into Kimi and Kimi Code plans, and implications for inference infrastructure demand.
10%
Distillation accusations and geopolitics
US government and OpenAI allegations of distillation and reasoning extraction by Moonshot, chip access concerns, and the US-China open model race.
10%
Moonshot company, funding and founder
Moonshot AI fundraising and valuation, CEO Yang Zhilin's background and views, and market impact on frontier labs' business models.
10%
Tone and stance
SentimentPositive leads
Author postureSupportive leads
Performance benchmark
- Median likes
- 103
- Median reposts
- 6
- Median replies
- 11
- Median views
- 10.5K
Posts with media make up 74% of this collection. Their median all-time score is 17.2, compared with 9.21 for text-only posts.
Format mix
- Announcement38% ยท score 12.9
- Opinion24% ยท score 26.0
- List12% ยท score 6.92
- Case Study8% ยท score 211.1
Consensus and debate
Where creators agree, and where they do not
Shared view
K3 framed as a frontier-class open-weight release
Many posts frame K3 as close to or matching closed frontier models. Kimi.ai announced the weights and tech report for a 2.8T MoE with a 1M-token context. Guillermo Rauch called it the first open model ahead of all proprietary ones on a web engineering benchmark. Ahmad placed it a couple of points below Fable 5 and GPT 5.6 Sol Max on the Intelligence Index.
Shared view
Sparse MoE design details are widely cited
Posts repeatedly cite 896 experts with 16 active per token, KDA attention, and a Moonshot-reported 2.5x efficiency figure relative to K2. Tech with Mak notes the figure is a scaling-efficiency improvement, not a universal 2.5x speedup.
Shared view
Demand outstripped Moonshot's GPU capacity
Kimi.ai paused new subscriptions to protect existing members and split membership into separate Kimi and Kimi Code plans. Mark Kretschmann read this as a sign of a very successful launch. Deirdre Bosa framed K3 as bullish for the infrastructure layer.
Shared view
Praised for coding, agents and 3D/frontend
Users highlight agentic coding benchmark placements, Agent Swarm, and the open-sourced Kimi Code CLI. In one head-to-head test, Bhavy reported a far richer 3D scene than Opus 4.8. Vaishnavi shared a Blender example in which the model critiqued and rebuilt its own output.
Open debate
Cheap alternative or token wastrel?
Miles Deutscher pitches K3 as a cost saver at $3/$15 per million tokens with a 90% cache discount. Harshil Tomar reports a 75% cheaper workflow using Kimi and GLM. Gavin Baker counters that, per Artificial Analysis, K3 is 50-70% more expensive to run than GPT 5.6 because of token inefficiency. Emad Mostaque, quoted by Rohan Paul, expects costs to drop 10 to 50 times as serving is optimized.
Open debate
Benchmark hype vs harness and saturation caveats
Ethan Mollick warns that conclusions rest on fairly saturated benchmarks and ELOs. Simon Willison notes the pelican benchmark is detached from what matters. Julian Goldie SEO points out that K3 was tested in Moonshot's harness while the GPT model was tested in Codex. Andon Labs found that Kimi K2.5's Vending-Bench 2 results depended on which API served it.
Open debate
Is 'open' practically open?
Nathan Lambert describes the license as MIT-inspired, with companies above $20M/yr needing a commercial deal. Layton Gott mocks the hardware needed to run K3 locally. Other posts show workarounds: Tech with Mak describes a CPU run at 8.24 GB of resident RAM and about 26.5 s/token, and Vaishnavi reports a 16x DGX Spark cluster.
Open debate
Distillation allegations
Director Michael Kratsios alleged that Moonshot distilled Anthropic's Fable and accessed GB300s. Mark Kretschmann relayed OpenAI's report attributing reasoning-extraction attempts to individuals associated with Moonshot, noting these were attempts, not confirmed extractions. Ahmad argued Fable 5 was unavailable for most of K3's training.
What performs
Patterns behind standout posts
Official Kimi posts top the score rankings
The K3 weights release (all-time score 4642.08, 313.02x the median) and the subscription pause (2183.07, 147.21x) are the top two outliers. Kimi_Moonshot's median all-time score is 3412.57.
Geopolitics and company themes score higher
The distillation/geopolitics theme has a 54.265 median score and the Moonshot company/founder theme 127.2. Both are well above coding/agents at 9.213, the largest theme at 38% of posts. The first two themes have only 5 posts each, and themes overlap.
Case studies post the highest format median
CASE_STUDY posts have a 211.13 median score, versus 12.879 for announcements and 6.92 for lists, though only 4 posts carry that label. Examples include the Opus 4.8 comparison and the CPU deployment teardown.
Media posts score higher
Posts with media have a median score of 17.23 versus 9.213 for text-only posts. 74% of posts include media.
Pricing theme has the lowest median score
The pricing and efficiency theme covers 24% of posts but has the lowest theme median score, 7.92. It includes BridgeMind's complaint about 24 tokens per second.
Statistical standouts
- View standout post 1Score 4642.1 ยท 313.02ร median
- View standout post 2Score 2183.1 ยท 147.21ร median
- View standout post 3Score 1591.7 ยท 107.33ร median
- View standout post 4Score 1337.8 ยท 90.21ร median
- View standout post 5Score 1042.6 ยท 70.31ร median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
1. Vaishnavi
@_vmlops
2 posts
2. Alex Veremeyenko
@alex_verem
2 posts
3. Julian Goldie SEO
@JulianGoldieSEO
2 posts
4. Kimi.ai
@Kimi_Moonshot
2 posts
5. Mark Kretschmann
@mark_k
2 posts
6. Nathan Lambert
@natolambert
2 posts
Posting is spread across many voices
40 creators produced 50 posts, and the top five account for 20% of placements. No listed top voice has more than 2 posts.
Analyst voices add skeptical framing
Nathan Lambert (median score 221.32) contributes license analysis and a podcast on open models. Gavin Baker and Ethan Mollick add caveats on cost and benchmark saturation.
Low-scoring accounts push setup tips
Julian Goldie SEO, with a median score of 0.77, posts a Hermes/MCP setup outline and model-routing advice. These score far below the analytical and official posts.
Since the previous snapshot
What changed since Aug 25, 2026
- 56% of the selected posts remained.
- The creator count changed by -1.
- The leading sentiment remained stable.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best Kimi tweets
Top Kimi tweets from 40 creators
Ranked 01โ50
- 01
@Kimi_Moonshot ยท
Releasing the model weights and technical report of Kimi K3. Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window. New model architecture: 2.5x the intelligence per unit of compute, not just more params. Alongside Kimi K3, we're opening up more of the stack behind it โ high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale. Model weights: https://t.co/7m7eEg6Y0B Tech report: https://t.co/yeu6cjpMCT Tech blog: https://t.co/YTfiMSNM1f
- 1.2KReplies
- 6.1KReposts
- 37.6KLikes
- 7.9MViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 02
@Kimi_Moonshot ยท
Kimi K3 has received far more love than we expected, and our GPUs are feeling it. Over the past 48 hours, demand has pushed close to the limits of our current capacity. To protect the experience of existing subscribers, we're temporarily pausing new subscriptions and prioritizing compute for current members. Existing subscribed users are not affected. We're adding capacity as fast as we can and will reopen new subscription spots in batches. Going forward, we'll also split membership into two more focused plans: Kimi Membership for Kimi Web, App, and Work; and Kimi Code Membership for coding workflows. This will help us match compute more precisely and keep the experience stable. Thank you for your patience and understanding!
- 1.5KReplies
- 2.6KReposts
- 36.7KLikes
- 12.6MViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 03
@mkratsios47 ยท
We have information that Moonshot AI distilled Anthropicโs Fable for the development of its K3 model. To do this they developed a sophisticated internal platform to conduct large scale distillation against U.S. models, allowing them to quickly switch between multiple methods of access to avoid detection. Moonshot AI has also acquired GB300-equipped servers and has accessed GB300s in Thailand, likely to train its AI models. ย The United States strongly supports the free and fair development of AI, including a thriving competitive ecosystem that spans frontier models, specialized systems, open-source frameworks, and open-weight models. Legitimate AI distillation used to create smaller, more efficient models plays a vital role in this open innovation ecosystem. However, large-scale, covert industrial distillation aimed at stealing proprietary U.S. technology and undermining American research is unacceptable.
- 3.9KReplies
- 1.3KReposts
- 14.8KLikes
- 9.4MViews
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@GavinSBaker ยท
Kimi K3 may be an important inflection point for AI. Potentially negative for Anthropic and OpenAI while being net positive for essentially every other company in the world. I mean that very literally. Although the real โSputnik momentโ would be an open-source frontier model that was also token efficient unlike Kimi K3 which is 50-70% more expensive to run than GPT 5.6 per Artificial Analysis. Rationale: ย A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs. Those labs would become monopsonies for power, data centers, semiconductors and hyperscalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application/software layers.ย ย Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software. ย This is why Jensen is so supportive of open-source. An open-source model requires the *exact* same amount of compute to run as a closed frontier model of similar size and architecture.ย Kimiย K3 is roughly the same price as GPT 5.6ย Terra on a per token basis, which actually suggests that it is less computationally efficient as I am sure that GPT 5.6 is priced to a higher margin than K3. And given that K3 is a token wastrel, i.e. token inefficient, it is significantly more expensive per task than GPT 5.6 and Grok 4.5, which are much more token efficient. Cost per token and token efficiency (i.e. intelligence density per token) are the drivers of intelligence per unit of cost. The winning AI companies will be those that offer the most intelligence per $ over time. ย Lower margin % at the model layer = more margin $ at every part of the infrastructure layer and is a godsend for software. This can happen either through open-source models like K3 at the frontier *or* having a vertically integrated model company like Meta, SpaceX or Google at the frontier. Both outcomes result in a lower margin % at the model layer as vertically integrated model companies donโt really care where the margin $ come from. This is why it was so painful for OpenAI and Anthropic when Google was right there with them from a model competitiveness perspective and why Grok 4.5 and Muse 1.1 were just as important as Kimi K3. โจThe reason Kimi K3 is only *potentially* negative for Anthropic and OpenAI is 1) the @ericvishria point that the Claude and ChatGPT products and harnesses may be more important than their models today and 2) the hypothesis that they have much more advanced model checkpoints internally that are already being used for RSI. In the latter scenario, reaching RSI even a few months ahead of other labs might be enough to cement a permanent lead. Time will tell on both points. And likely fairly quickly. Caveat would be that since Kimi K3 is not token efficient and thereby actually more expensive than ChatGPT 5.6, we may need to see a more token efficient open-source model at the frontier or see Grok 5/Composer 4/Muse 2 at multiple points on the Pareto frontier for this potential risk to Anthropic and OpenAI to play out. And I am sure they will both vertically integrate as quickly as possible while continuing the product/harness strength they have shown over the last 8 months.
- 548Replies
- 1.3KReposts
- 8.1KLikes
- 2.4MViews
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@Bhavani_00007 ยท
I tested Kimi K3 vs Claude Opus 4.8 Same prompt, an armory bay with lighting, props, and detail. Top is Kimi K3, bottom is Opus 4.8. It's not even close. Kimi K3 built a full scene with textures, proper lighting, ammo crates, weapon racks, working detail everywhere. Opus 4.8 gave me a near empty room with a couple of floating tables. No doubt it beats Opus 4.8. Kimi K3 is Fable 5 level, and it's clearly better than GPT-5.6 Sol at 3D and games. An open weight model just matched the best closed models on the market. Let that sink in.
Watch video- 188Replies
- 590Reposts
- 5.3KLikes
- 439.6KViews
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@rauchg ยท
Kimi K3 is the best performing model on https://t.co/aporqgIfIh, ahead of Fable, reaching a comparable success rate in less time. This is the first time that an open model is ahead of all proprietary ones for this comprehensive web engineering benchmark. Notes: โช๏ธ Benchmarks donโt always tell the full story, although this is important signal, adding to mounting evidence that this could be a breakthrough moment for open models โช๏ธ No model as of yet has reached 100% completion on this set of evals. The top performer peaks at 92% and 96% โwith helpโ
- 165Replies
- 388Reposts
- 5.8KLikes
- 603.1KViews
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@techNmak ยท
Someone wrote a 176 KB C program that runs Kimi K3, a 2.78 trillion parameter model, on one CPU with 8.24 GB of RAM. I went through the repo because that number sounded impossible. The checkpoint hasnโt been chopped down to 8 GB, itโs still 1.56 TB. K3 just happens to have an architecture that gives you some interesting places to put all those bytes. Most of the model is the MoE. There are 92 routed layers with 896 experts each, but only 16 experts are used per layer for a token. That gives you 82,432 routed experts in total, occupying 1.447 TB, or 93% of the checkpoint. They stay on NVMe instead of sitting in RAM. One expert is about 17.56 MB, so a token can require 1,472 expert fetches across those 92 layers. With no useful resident hits the engine reads about 25.83 GB of expert weights per token. That still leaves ~113 GB of weights which are used all the time. The author repacks 108.81 GB of those layer weights into a separate "trunk", laid out so each of the 93 layers can be read in one contiguous run. Whatever layers fit in RAM stay pinned, the rest are streamed through a reusable buffer as the forward pass walks from layer 0 to 92. With more RAM you pin more of the trunk. With less RAM you read more of it from disk. K3 helps again with its attention design. 69 of its 93 layers use KDA, which carries a fixed-size recurrent state instead of a KV cache that grows with every token. The other 24 use MLA, where the cached representation is much smaller than storing full per-head keys and values. So 8.24 GB is not the size of Kimi K3. Itโs the measured peak amount that has to be resident at once when the rest of the model is allowed to live on disk. And of course this is slow. Their 8 GB measurement is around 26.5 seconds/token. That almost makes the result more interesting to me, they didnโt make 1.56 TB disappear, they made RAM stop being the requirement that decides whether the model can run at all. Here's the GitHub Repo: https://t.co/OZdtBrLgJa
- 19Replies
- 79Reposts
- 647Likes
- 40.2KViews
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@natolambert ยท
Kimi K3 license. It's inspired by MIT but distinctly non-commercial, where any company making over $20M/yr must get a specific commercial deal (and display Kimi K3 if over 100M users or $20M/mo revenue)
- 191Replies
- 262Reposts
- 3.7KLikes
- 526.5KViews
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@CodeByPoonam ยท
๐จBREAKING: Kimi just raised $1 billion. Again. Thatโs three rounds in under 90 days. The Kimi story is getting wild. $18 billion valuation. Up 4x in three months. Let that sink in. Moonshot is now: โ The fastest Chinese AI company to cross $10 billion โ The first Chinese LLM startup to close three consecutive rounds in under 90 days โ Still raising. A fresh $1 billion round is in progress right now. This isnโt a funding story. Itโs a signal. Investors arenโt just betting on Kimi. Theyโre betting that Chinaโs AI race is far from over, and that Moonshot is one of the horses that finishes. While everyoneโs been watching OpenAI and Anthropic, Moonshot quietly became one of the most aggressively funded AI companies on the planet.
- 110Replies
- 200Reposts
- 2.3KLikes
- 174KViews
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@rohanpaul_ai ยท
This is Moonshot AI/Kimi K3's founder & CEO Yang Zhilin at GTC 2026 The 34-year-old researcher studied at Tsinghua, then earned his Carnegie Mellon Ph.D. Then he worked with Jason Weston at Meta AI and Quoc Le at Google Brain. His work also connected him to Yoshua Bengio and Yann LeCun on the topics of reasoning and representation. Eventually, Yang returned to China and co-founded Moonshot AI in Beijing a few years later. His earlier Transformer-XL and XLNet work addressed how language models forget distant context. Moonshot AI carried that research focus forward through Kimi K2โs 1T-parameter mixture-of-experts design. XLNet, was later cited more than 10,000 times and is still his best-known academic paper. Kimi K3 expands the formula with native vision, 1M-token context, and only 16 active experts. His career has become a textbook example of how quickly global research networks can reshape the way countries compete in AI. ---- From "Kimi AI" YouTube channel, (full video link in comment)
Watch video- 26Replies
- 81Reposts
- 648Likes
- 60.1KViews
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- 11
@simonw ยท
My notes on Kimi K3, plus some thoughts on what we can still learn from the pelican benchmark even while it becomes further detached from how good the models are at the things that matter (like agentic tool calling across longer conversations) https://t.co/F4AicvxAmq
- 29Replies
- 42Reposts
- 570Likes
- 51.1KViews
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@bridgemindai ยท
Kimi K3 has one problem and it is a BIG one. It is slow. Painfully slow. 24 tokens per second average. Nearly 6 seconds of latency before it even starts. Look at these charts. Vibe coding is iteration. Prompt, review, refine, repeat. At 24 tok/s the loop breaks. You send a prompt and go make coffee. The model is brilliant. Waiting for it is agony. Moonshot built a frontier model and put it on budget compute. Fix the serving, and Kimi K3 takes over the world.
- 86Replies
- 17Reposts
- 391Likes
- 21.3KViews
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@natolambert ยท
New podcast with @xeophon on all things open models. More on Kimi K3, Qwen 3.8, GLM-5.2, Xi's WAIC speech, distillation, the open-closed Gap, and what's next. Chapters: 00:00 Welcome & context 04:38 Living with / using Kimi K3 08:53 GLM 5.2โs continued role 12:47 How are the Chinese models this good? 17:41 Data, environments, and a tour of the Chinese labs 19:47 Roundup of Chinese providers: Qwen, DeepSeek, MiniMaxโฆ 24:08 The US open-model ecosystem 30:25 Frontier vs. near-frontier, and the cybersecurity case against bans 34:58 Distillation and the Ben Thompson debate 44:12 Predictions and a frontier tier list 48:36 Wrap-up Hoping to keep doing a few more of these on @interconnectsai. Crucial times in AI, we're working hard to share our expertise.
Watch video- 8Replies
- 32Reposts
- 224Likes
- 18.5KViews
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@emollick ยท
A lot of swift conclusions are being drawn about Kimi K3 based on fairly saturated benchmarks and ELOs, rather than actually testing it on very hard problems. The AI frontier has already moved so far that a good model that is a still months behind looks like the future to many.
- 52Replies
- 36Reposts
- 681Likes
- 71.7KViews
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@TheAhmadOsman ยท
Kimi K3 is only a couple of points below Fable 5 and GPT 5.6 Sol Max on the Intelligence Index Fable 5 wasn't even available for majority of time this model was being trained so they cannot scream "distillation" Opensource AI is now Frontier Intelligence.
- 47Replies
- 70Reposts
- 755Likes
- 152.5KViews
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@modal ยท
Kimi K3 is live on Modal. Moonshot has shipped the world's first open 3T-class model, and we're a day zero launch partner. We trained a custom DFlash speculator for K3's novel architecture so you can run it faster, losslessly. The most capable open model we've worked with by far.
Watch video- 34Replies
- 57Reposts
- 674Likes
- 367.2KViews
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@dee_bosa ยท
the market has evolved and Kimi K3 is bullish for the infrastructure layer "Thereโll be an interesting, almost thermonuclear battle to provide the compute to support the demand thatโs emerging for open weight models" -Benchmark's @peterfenton Deepseek 1.5 yrs ago made everyone question how much compute AI would actually need. K3 points the other way -- cheaper, open models mean wider distribution, more usage, more inference
Watch video- 28Replies
- 22Reposts
- 241Likes
- 38.9KViews
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@_vmlops ยท
Someone asked Kimi K3 to build a fighter jet in Blender It rendered the result, inspected its own image, realized it looked too boxy and even noticed the triangle count didn't fit the target era So it scrapped everything and rebuilt it That's the kind of iterative AI that actually gets better
Watch video- 4Replies
- 16Reposts
- 181Likes
- 22.7KViews
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@milesdeutscher ยท
Kimi K3 is one of the best models you can run in your Hermes agent. It's literally perfect for agent workflows (90% token discount). It even beats (or matches) Fable 5 AND GPT-5.6 Sol on several benchmarks while being cheaper. Pairing these two is the best cost-to-performance setup you can run: Why Kimi K3 โ Native vision โ 1M token context. โ On several benchmarks (FrontierSWE, MCPMark, Harvey Lab-AA), it's actually beating Claude Fable 5 and GPT-5.6 Sol Pricing API pricing: $3/$15 per million tokens (roughly Claude Sonnet territory and about half the output cost of GPT-5.6 Sol). Cached input drops to $0.30/million, a 90% discount, which matters a lot for agent workflows that reuse the same system prompt and context on every call. Pairing with Hermes Hermes already connects to 300+ models through OpenRouter. I recommend setting Kimi K3 as your default instead of paying Frontier prices for every task. Inside Hermes, point your model provider to moonshotai/kimi-k3 via OpenRouter, or connect directly through Moonshot's OpenAI/Anthropic-compatible API at https://t.co/0Lmjdhj0i0. You can even send a simple prompt like "help me set up Kimi K3" and your agent will guide you through the entire process. The TL;DR is that you get near-frontier reasoning and agentic performance at Sonnet-tier pricing.
- 27Replies
- 13Reposts
- 145Likes
- 26.9KViews
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@ChrisGPT ยท
Kimi K3 is an absolute coding monster. Across six benchmarks, it takes 1st on Program Bench and SWE Marathon, 2nd on FrontierSWE, Terminal Bench 2.1, and Kimi Code Bench 2.0, and 3rd on DeepSWE even landing 0.5 points behind GPT-5.6 Sol on Terminal Bench 2.1!! - which I didnโt think theyโd show 2.1! Open Source is so back!
- 12Replies
- 11Reposts
- 182Likes
- 7KViews
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@goyalshaliniuk ยท
Kimiโs CEO ๐จ๐ณ says most AI labs are focused on the wrong thing. Zhilin Yang argues that models arenโt the real differentiatorโteams are. While labs like Anthropic double down on model performance, he believes the edge comes from how you organize the people building it. Moonshot put that belief into action. When demand spiked, they sold out plans intentionallyโprotecting user experience instead of squeezing more revenue. Around the same time, Anthropic reportedly cut usage limits during peak demand. His core idea: long context is the new RAM of the AI eraโjumping from 128K to massive scales in just two years, not decades. Bottom line: โThe real moat isnโt the modelโitโs the organization behind it.โ So what matters more: the model, or the team building it?
Watch video- 10Replies
- 11Reposts
- 50Likes
- 896Views
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@rohanpaul_ai ยท
Great explanation by Emad Mostaque, co-founder of Stability AI. "Weโll see the cost of Kimi K3 drop by 10 to 50 times, I think, over the next few months as it gets optimized. " Basically Kimi K3โs current inference cost is quite high, but that price reflects immature infrastructure, not a permanent technical limit. And that gap will not last long. US-based specialized infrastructure companies will optimize kernels, routing, quantization, batching, memory use, and serving systems around those models once the Kimi K3 weights are available. --- "Right now, it uses twice the number of tokens for the same task compared with GPT-5.6. Again, weโre going to see that cost drop because everyone and their dog is going to optimize the crap out of this. Fireworks has just raised funding at a $17 billion valuation, while others, such as Modal and Baseten, are valued at $10 billion. These are inference providers for open-source models. Theyโve all raised around a billion dollars, which theyโre now going to spend on optimizing the Chinese model, making it more efficient, and running it. American labs that handle the inference side of things are going to optimize the crap out of this. Therefore, we will see it catch up." ---- From "Peter H. Diamandis" YouTube channel, (full video link in comment)
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@EXM7777 ยท
kimi K3 has that big model smell it must be genuinely embarrassing at OpenAI and Anthropic HQ that a chinese model writes better english than their frontier models lmao it's also #1 on frontend arena right now, and just overall better at figuring things out... the unclear, nuanced asks where models usually flail very pleasant to talk to, runs fine inside claude code... big win for china
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@akshay_pachaar ยท
Kimi K2.6 just dropped. And it crushed Claude Opus 4.6 on SWE-Bench Pro. - Kimi K2.6: 58.6 - GPT-5.4 xhigh: 57.7 - Gemini 3.1 Pro: 54.2 - Claude Opus 4.6: 53.4 An open-source model is now #1 on agentic coding. The moat around frontier labs is shrinking fast.
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@mark_k ยท
Demand for Kimi K3 has pushed @Kimi_Moonshot close to the limits of its current GPU capacity. New subscriptions are temporarily paused while more capacity is added. Existing subscribers are unaffected. Kimi is also splitting memberships into a general Kimi plan and a separate Kimi Code plan to allocate compute more efficiently. A strong indication that K3 has been a very successful launch.
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@CodeByNZ ยท
๐ Kimi K3 is now live. Moonshot AI has released its new flagship model, Kimi K3, which is already appearing in the Kimi app, CLI, and desktop version. The standout feature is K3 Agent Swarm, which supports massive parallel search and batch processing allowing users to get significantly more done in a single session. The model builds on Kimiโs reputation for strong agentic performance and long-context handling. Early users are already testing it across coding, research, and multi-step workflows. Itโs one of the more interesting releases from a Chinese lab in recent weeks.
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@techNmak ยท
Kimi K3 has 2.8 trillion parameters. But during its sparse expert computation, it effectively activates only 16 out of 896 experts. In other words, 880 experts sit out. At first, this sounds like the entire trick: Build an enormous model. Use only a small part of it at once. Get more capacity without paying for all of it on every computation. Then you try to train it. Now the problem is no longer just how many experts the model has. The problem is deciding which experts should receive the work, and making sure that decision does not destroy the efficiency sparsity was supposed to create. Imagine a company with 896 specialists. Every incoming problem is assigned to a small team of 16. The company looks incredibly efficient. Until work starts accumulating around a few specialists while hundreds of others remain underused. The company still has 896 people on paper. But its actual throughput is determined by the overloaded few. This is the hidden problem inside sparse MoE models. Adding experts increases the modelโs total capacity. Activating fewer experts limits how much of that expert capacity participates in the same computation. But the more selective the system becomes, the more important routing and balancing become. Moonshot describes them as first-order challenges at Kimi K3โs level of sparsity. Its answer is a technique called Quantile Balancing. K3โs routing system produces scores that are used for expert allocation. Instead of relying on repeatedly adjusted heuristic updates, Quantile Balancing derives the allocation directly from quantiles of those router scores. A quantile tells you where a value sits relative to the rest of a distribution. Moonshot says this approach removes the heuristic updates and one sensitive balancing hyperparameter. The complete technical report is not public yet, so the exact algorithmic details stop there. But solving the mathematical allocation is still not enough. The experts do not exist in an abstract diagram. They run across physical accelerators. An allocation that becomes imbalanced across experts can also create an imbalanced distributed system, reducing throughput at large expert-parallel scales. So Moonshot introduced a fully balanced expert-parallel training method with static shapes and no host synchronization on the critical path. The goal is explicit: prevent expert imbalance from degrading throughput. This is the part most model announcements hide behind the parameter count. At this scale, a model is not only learning representations. It is also scheduling computation. It must decide which expert capacity participates, balance that allocation, distribute the resulting work across hardware, and preserve throughput while doing it. A useful way to think about Kimi K3 is: 896 experts provide the capacity. 16 are effectively activated in the sparse expert computation. Quantile Balancing helps allocate them. Expert-parallel training helps execute that allocation efficiently across hardware. And this is only one part of K3โs scaling architecture. Kimi Delta Attention provides an efficient foundation for scaling attention. Attention Residuals selectively retrieve representations across model depth rather than accumulating them uniformly. Stable LatentMoE increases expert sparsity. Different mechanisms attack different limits to scaling. Together with refinements to training and data, Moonshot reports that these structural changes deliver roughly 2.5ร the overall scaling efficiency of Kimi K2. That is Moonshotโs reported scaling-efficiency improvement. It does not mean K3 is universally 2.5ร faster or 2.5ร more capable. Most people will remember that Kimi K3 has 2.8 trillion parameters. I think the more interesting number is 16. Because at this scale, the difficult part is no longer simply building more capacity. It is making a tiny fraction of that capacity work in the right place, without losing the efficiency to coordination.
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@andonlabs ยท
Kimi K2.5 on Vending-Bench 2. Once again, it matters which API you use. It makes twice as much money when using @Kimi_Moonshot official API compared to @FireworksAI_HQ. 2nd best open source model.
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@Hartdrawss ยท
I've made my Entire Workflow 75% Cheaper using KIMI + GLM Here's a quick breakdown of Full Workflow : 1. Code generation - claude sonnet 4.6 โ kimi k2.7 code - benchmark : k2.7 actually edges sonnet 4.6 on the artificial analysis intelligence index - price: blended $0.72/M vs $2.31/M, roughly 3x cheaper 2. heavy reasoning / hardest tasks - claude opus 4.8 โ kimi k3 - benchmark gap: roughly 7-8 points behind on swe-bench, opus still wins here - price: $4/M output vs $25/M, about 6x cheaper 3. agent loops / tool calling - claude sonnet 5 โ glm 5.2 - benchmark gap: under 1.5 points apart on the benchmarks that actually matter, sonnet still wins on tool calling specifically - price: roughly $550/mo vs $1,350/mo at standard usage, close to 2.5x cheaper 4. bulk / high volume tasks - gpt-5.6 โ kimi k3 - benchmark gap: good enough for ticket triage, agent loops, anything that isnt the hardest edge case - price: output tokens run close to 7.5x cheaper This is overall from 10-14 days of testing and observation. Tbh I still haven't moved completely over to them but crazy that open source model's are cut to cut with new frontier tools ! i am thinking to go in till August end on them and make a deeper drop on the breakdown of stuff
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@zaimiri ยท
I ran a 12-hour autonomous coding session with Kimi K2.6. It rewrote a financial engine thread topology after analyzing CPU flame graphs. 185% throughput improvement. > Open source > 300 agents > $0.60/M input tokens. Here's what this actually does:
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@mark_k ยท
A coordinated campaign tried to extract hidden reasoning from @OpenAI's models, according to a new report. The company attributes a core cluster to individuals associated with Moonshot AI, the company behind Kimi. The technique is pretty wild: operators copied encrypted reasoning from one conversation into another, then asked the model to decrypt and transcribe it. The goal was to collect internal reasoning that could help train competing models. OpenAI recorded 16,000 extraction attempts during a two-day spike in July and identified related activity across more than 15,000 users. OpenAI says it closed the extraction pathway and banned or restricted accounts involved. Those numbers represent attempts, not confirmed successful extractions, and it hasn't attributed the entire campaign to Moonshot. Still, that's a very direct accusation against the people behind one of China's leading AI models.
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@pankajkumar_dev ยท
Kimi K3 Launched - Kimi K3 is now live the largest open-source MoE model yet with 2.8T parameters and a 1M context window. - API pricing: $3/$15 per 1M tokens ($0.30 cache hits), around 5ร more expensive than K2.7 - Benchmarks are impressive: #2 on AA-Briefcase, 91.2 on BrowseComp (SOTA), and behind only Claude Fable 5 Max & GPT-5.6 Sol Max on GDPval-AA v2. - Frontend generation is exceptional. From my testing, it beats Opus 4.8 and is close to Fable 5 in UI taste. - Built on a new KDA (Kimi Delta Attention) + AttnRes architecture with 896 experts (16 active/token) for highly efficient sparse routing.
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@JaynitMakwana ยท
Most coding models get stronger by thinking longer. More tokens. More compute. Higher cost. Kimi did the opposite. Kimi K2.7 Code is delivering better coding results while using fewer tokens than K2.6. That is harder to pull off than another benchmark win. Here is the full breakdown ๐
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@Layton_Gott ยท
How AI influencers talk about open source: โKimi K3 is 100% FREE and open source. To actually run it fully local you ONLY need: โข 1.4TB of VRAM โข Around 18 H100s, so roughly $500k in GPUs โข A server rack with industrial cooling โข About 13 kilowatts running nonstop, which is ten houses worth of power And thatโs all it takes to have your very own local model that competes with frontier modelsโ
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@pankajkumar_dev ยท
- Kimi K3 frontend output looks very impressive, and Moonshot seems to have done something special with UI generation. - Based on this generation, it looks capable of competing with Fable 5 and Gemini 3.5 Pro in design taste. - This particular generation took around 35 minutes, the model spends a lot of time reasoning. - Moonshot should optimizes this, more reasoning also means higher token usage and cost.
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@RoundtableSpace ยท
Moonshot AI open-sourced Kimi Code CLI as a free terminal agent powered by Kimi K3 that supports video inputs and dedicated subagents.
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@VaibhavSisinty ยท
Kimi K3 just launched. China's biggest open-weights model yet. And the numbers are genuinely wild. ๐คฏ 2.8 trillion parameters. 896 experts, 16 active per token. 1 million token context window. Available right now. Two variants at launch: K3 Max for general reasoning and coding. K3 Swarm Max for running hundreds of parallel agents on a single task. One-shotted a 3D paper plane game with full physics. Testers called it Fable 5 level on frontend tasks. 91.2 on BrowseComp. Competitive with Opus 4.8 across coding and knowledge benchmarks. Trading blows with GPT 5.6 Sol on several evals. Agent Swarm decomposes your task into subtasks, spawns specialized sub-agents for each, and coordinates them simultaneously. Full-stack apps in parallel instead of sequentially. Multi-hour autonomous coding runs. Thousands of tool calls. Self-testing. Self-debugging. Writes, runs, iterates without human input. Roughly $3 input and $15 output per million tokens. A fraction of what frontier Western models charge for comparable performance. Full weights expected to be open-sourced in the coming days. Six months ago, Chinese models failed basic spatial reasoning tests. Today one of them is matching the best models on the planet. At a fraction of the cost.
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@pukerrainbrow ยท
OpenAI called his model "full AI communism" > dreamed of being a rock star and a wandering poet > won math olympiad prizes instead > got into Tsinghua, China's MIT, started a band there > PhD at Carnegie Mellon, finished in four years > built a model as a student that beat Google's own > Google Brain and Meta AI both hired him > went back to China > co-founded Moonshot AI in 2023 with his band's lead guitarist > named the company after Pink Floyd's Dark Side of the Moon > named one of his AI agents OK Computer, after Radiohead > built Kimi, a chatbot that can read seven novels in one prompt > spent over a year in DeepSeek's shadow > last month released Kimi K3, largest open-weight AI model in the world > released it for free > tech stocks sold off in the US > Elon Musk called it "impressive" > the White House says he trained it on banned Nvidia chips > $300M to $35B in three years > and he's 33 His name is Yang Zhilin
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@amir ยท
PSA this Google Cloud post was incorrectly interpreted by some folks on X (am not naming names) to mean Google is paying Moonshot to host Kimi K3. A Google source says nope, it's just information for customers to self-deploy Kimi K3 using Google Cloud services if they want to. https://t.co/kuLvwXiMgx
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@icreatelife ยท
Moonshot AI releases weights for Kimi-K3, open-weight model performs almost as well as frontier models while being 2-3x easier to run
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@alex_verem ยท
Moonshot AI open-sourced Kimi Code CLI. A terminal coding agent that takes video as input. Drop a screen recording into the chat. The agent processes it and generates code from what it sees. Yes, a full video clip. Conversational MCP setup. Type /mcp-config and talk to add servers. No hand-editing JSON files. Isolated subagents. Built-in coder, explore, and plan agents run in separate context windows. The main conversation stays clean. It works with Kimi models by default and can point to other providers. The features showing up in open source coding agents are worth tracking regardless of which tool you use.
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@shawnchauhan1 ยท
Moonshot AI just had to pause new subscriptions. Demand broke their GPUs. Kimi K3 launched days ago: 2.8 trillion parameters, a million-token context, native vision. It topped a major coding leaderboard against paid, closed frontier models. Full open-source weights land July 27, making it the largest open model ever released. A free, top-ranked coding model isn't a benchmark curiosity. It's a procurement decision. Every team still budgeting six figures for closed API calls should be nervous right now.
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@_vmlops ยท
FULL KIMI K3 (2.8T PARAMS) IS NOW RUNNING ON A 16ร DGX SPARK CLUSTER Someone just got the full Moonshot AI Kimi K3 model running.... no distillation, no shortcuts โ 21 TPS average โ 38 TPS peak decode โ 750 TPS prefill โ First successful run using Inferact's Kimi-K3-DSpark build โ vLLM image + setup guide coming soon A ~$100K DGX Spark cluster can now self-host a 2.8T-parameter frontier-class model The era of sovereign AI infrastructure is arriving faster than most people expected
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@RoundtableSpace ยท
KIMI K3 BEATS GPT-5.6 SOL ON 3D AND PHYSICS AT 9X LOWER COST Running identical prompts and harnesses for 3D generation and physics logic, Moonshot AI's Kimi K3 outperformed GPT-5.6 Sol while slashing total execution costs from $2.15 down to $0.23.
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@JulianGoldieSEO ยท
Kimi K3 is not just another open-source AI model. It can build polished games, operate tools, and improve its own work. Hereโs the smartest setup: โ Plug Kimi K3 into Hermes Agent โ Connect your Obsidian memory โ Add MCP tools like Blender โ Create a dedicated Kimi profile โ Run scheduled workflows automatically Now your model can code, research, create, and remember.
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@MartinSzerment ยท
Kimi K2.6 from Moonshot AI just changed the game. It doesn't end a session with a chat reply. It ends with a folder of deliverables PDF, PowerPoint, Excel spreadsheet, 7000-word research paper. All from a single prompt. Under the hood: 1 trillion parameters, 300 sub-agents working in parallel across 4,000 coordinated steps. The model can code autonomously for 13 hours straight. This isn't a chatbot. It's a coordinated analyst team packed into one model. The kicker? It's open-source, and API costs are a fraction of Claude or GPT. Chinese AI stopped chasing. It's now defining a new category output scale, not chat scale. Not how many replies per minute, but how many finished work products per prompt.
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@alex_verem ยท
What if the next jump in AI doesn't come from spending more on compute but from wasting less of it? Moonshot just dropped the technical report for Kimi K3. The whole timeline is reporting 2.8 trillion parameters, and that makes sense. It's the biggest open-weight model anyone's released. But the number I can't stop thinking about is 2.5x. Their report shows Kimi K3 gets 2.5x the intelligence per unit of compute compared to its predecessor. They didn't do it by adding more GPUs or burning more energy. They rethought the architecture from scratch. Their new design activates only 16 out of 896 experts per token, routing computation where it matters instead of firing the whole model on every input. That's not an incremental improvement. That's a rethink of how scaling works. The AI race so far has been a brute-force energy arms race. Bigger models demand more data centers and more power. Training GPT-3 alone burned the energy equivalent of powering 120 US homes for a year. The models built since then are orders of magnitude bigger. This paper argues the path forward isn't more brute force. It's better architecture. And that matters for reasons beyond benchmarks. If AI is going to run at the scale we're building toward, billions of queries across billions of devices, the energy math has to change. 2.5x efficiency gains compounded across billions of daily inferences is the difference between AI that's sustainable and AI that's a climate liability. The biggest open-weight model released to date might also be the strongest argument for doing more with less.
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@mjamilmoughal ยท
Moonshot just open-sourced Kimi K3, the largest open-weight model ever. 2.8T-parameter MoE (โ104B active), 1M context, native vision. Weights dropped yesterday on Hugging Face. It sits right behind Claude Fable 5 and GPT-5.6 Sol on independent benchmarks, and leads some coding arenas. This is frontier-scale open weights. The kind that actually shifts the landscape. Reality check: itโs massive. Even multiple DGX Sparks canโt run the full thing locally. But itโs live on Ollama Cloud (kimi-k3:cloud) with Pro or Max plans. Expect heavy traffic and slower responses for a while. Open models this strong change the calculus. The real edge now isnโt just access, itโs knowing exactly which model wins at which job.
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@thenewstack ยท
Moonshot AI releases Kimi K3 open weights on Hugging Face, but the 2.8T-parameter model demands massive GPU clusters โ reshaping the economics of open-weight AI. https://t.co/cy5rtXhlsA
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@JulianGoldieSEO ยท
KIMI K3 IS ONLY 0.5 POINTS BEHIND GPT 5.6 SOUL ON ONE OF THE HARDEST CODING TESTS. But that number doesn't tell the real story. Reality check: โ TerminalBench 2.1: Kimi K3: 88.3 GPT 5.6 Soul: 88.8 Soul Ultra: 91.9 โ Kimi K3 brings a 1M-token context window. โ Native vision lets it write code, inspect screenshots, and fix its own UI. โ Only 16 of 896 experts activate at once, making the model far more efficient. Where they separate: โ GPT 5.6 Soul wins on polish, design judgment, and professional coding workflows. โ Kimi K3 shines on huge codebases, long engineering sessions, and technical depth. The biggest mistake? People compare benchmark numbers without comparing the testing environment. K3 was tested in Moonshot's own coding harness. Soul was tested in Codex. Different tools. Different conditions. Different results. The smarter play isn't choosing one model. It's routing each job to the model that's best suited for it. That's where the real productivity gains come from.
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